A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

• Published in: Energies (Basel) 2021-03, Vol.14 (5), p.1328
• Main Authors: Zhou, Jianguo, Wang, Shiguo
• Format: Article
• Language: English
• Subjects: Algorithms; Bandwidths; Carbon dioxide; carbon price; Crude oil; Decomposition; Emissions; Emissions control; Emissions trading; empirical mode decomposition; Global economy; Global warming; kernel extreme learning machine; Learning algorithms; Neural networks; Prediction models; Prices; Search algorithms; secondary decomposition; sparrow search algorithm; Stochastic models; Time series; variational mode decomposition
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en14051328

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.